scholarly journals Identification of Coal Geographical Origin Using Near Infrared Sensor Based on Broad Learning

2019 ◽  
Vol 9 (6) ◽  
pp. 1111 ◽  
Author(s):  
Meng Lei ◽  
Zhongyu Rao ◽  
Ming Li ◽  
Xinhui Yu ◽  
Liang Zou
Keyword(s):  
Near Infrared ◽  
Geographical Origin ◽  
Learning Algorithm ◽  
Nir Spectroscopy ◽  
Chemical Compositions ◽  
Important Indicator ◽  
Quantitative And Qualitative Research ◽  
Quality Grading ◽  
Improved Model ◽  
Origin Identification

Geographical origin, an important indicator of the chemical composition and quality grading, is one essential factor that should be taken into account in evaluating coal quality. However, traditional coal origin identification methods based on chemistry experiments are not only time consuming and labour intensive, but also costly. Near-Infrared (NIR) spectroscopy is an effective and efficient way to measure the chemical compositions of samples and has demonstrated excellent performance in various fields of quantitative and qualitative research. In this study, we employ NIR spectroscopy to identify coal origin. Considering the fact that the NIR spectra of coal samples always contain a large amount of redundant information and the number of samples is small, the broad learning algorithm is utilized here as the modelling system to classify the coal geographical origin. In addition, the particle swarm optimization algorithm is introduced to improve the structure of the Broad Learning (BL) model. We compare the improved model with the other five multivariate classification methods on a dataset with 243 coal samples collected from five countries. The experimental results indicate that the improved BL model can achieve the highest overall accuracy of 97.05%. The results obtained in this study suggest that the NIR technique combined with machine learning methods has significant potential for further development of coal geographical origin identification systems.

scholarly journals Tracing the Geographical Origin of Durum Wheat by FT-NIR Spectroscopy

Foods ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 450 ◽  
Author(s):  
Annalisa De Girolamo ◽  
Marina Cortese ◽  
Salvatore Cervellieri ◽  
Vincenzo Lippolis ◽  
Michelangelo Pascale ◽  
...  
Keyword(s):  
Durum Wheat ◽  
Near Infrared ◽  
Southern Italy ◽  
Geographical Origin ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Classification Model ◽  
North Central ◽  
Linear Discriminant ◽  
The World

Fourier transform near infrared (FT-NIR) spectroscopy, in combination with principal component-linear discriminant analysis (PC-LDA), was used for tracing the geographical origin of durum wheat samples. The classification model PC-LDA was applied to discriminate durum wheat samples originating from Northern, Central, and Southern Italy (n = 181), and to differentiate Italian durum wheat samples from those cultivated in other countries across the world (n = 134). Developed models were validated on a separated set of wheat samples. Different pre-treatments of spectral data and different spectral regions were selected and compared in terms of overall discrimination (OD) rates obtained in validation. The LDA models were able to correctly discriminate durum Italian wheat samples according to their geographical origin (i.e., North, Central, and South) with OD rates of up of 96.7%. Better results were obtained when LDA models were applied to the discrimination of Italian durum wheat samples from those originating from other countries across the world, having OD rates of up to 100%. The excellent results obtained herein clearly indicate the potential of FT-NIR spectroscopy to be used for the discrimination of durum wheat samples according to their geographical origin.

scholarly journals The Ability of Near Infrared (NIR) Spectroscopy to Predict Functional Properties in Foods: Challenges and Opportunities

Molecules ◽  
2021 ◽  
Vol 26 (22) ◽  
pp. 6981
Author(s):  
Daniel Cozzolino
Keyword(s):  
Functional Properties ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Chemical Compositions ◽  
Food Ingredients ◽  
Wide Range ◽  
Challenges And Opportunities ◽  
New Instrumentation ◽  
Novel Algorithms ◽  
Main Routine

Near infrared (NIR) spectroscopy is considered one of the main routine analytical methods used by the food industry. This technique is utilised to determine proximate chemical compositions (e.g., protein, dry matter, fat and fibre) of a wide range of food ingredients and products. Novel algorithms and new instrumentation are allowing the development of new applications of NIR spectroscopy in the field of food science and technology. Specifically, several studies have reported the use of NIR spectroscopy to evaluate or measure functional properties in both food ingredients and products in addition to their chemical composition. This mini-review highlights and discussed the applications, challenges and opportunities that NIR spectroscopy offers to target the quantification and measurement of food functionality in dairy and cereals.

scholarly journals The Quality Control of Tea by Near-Infrared Reflectance (NIR) Spectroscopy and Chemometrics

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Ming-Zhi Zhu ◽  
Beibei Wen ◽  
Hao Wu ◽  
Juan Li ◽  
Haiyan Lin ◽  
...  
Keyword(s):  
Quality Control ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Chemical Compositions ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Control Quality ◽  
Tea Quality ◽  
Instrumental Technique

Tea is known to be one of the most popular beverages enjoyed by two-thirds of the world’s population. Concern of variability in tea quality is increasing among consumers. It is of great significance to control quality for commercialized tea products. As a rapid, noninvasive, and nondestructive instrumental technique with simplicity in sample preparation, near-infrared reflectance (NIR) spectroscopy has been proved to be one of the most advanced and efficient tools for the control quality of tea products in recent years. In this article, we review the most recent advances and applications of NIR spectroscopy and chemometrics for the quality control of tea, including the measurement of chemical compositions, the evaluation of sensory attributes, the identification of categories and varieties, and the discrimination of geographical origins. Besides, challenges and future trends of tea quality control by NIR spectroscopy are also presented.

Identification of geographical origin of TPFD based on handheld NIR spectroscopy and PLSDA

NIR news ◽  
2020 ◽  
Vol 31 (5-6) ◽  
pp. 25-29
Author(s):  
Rita-Cindy Aye-Ayire Sedjoah ◽  
Bangxing Han ◽  
Hui Yan
Keyword(s):  
Quality Control ◽  
Near Infrared ◽  
Geographical Origin ◽  
Nir Spectroscopy ◽  
Analysis Model ◽  
First Order ◽  
Standard Normal Variate ◽  
Standard Normal ◽  
Infrared Spectrometer ◽  
Near Infrared Spectrometer

The present study is focused on the identification of geographical origin (Zhejiang, Yunnan and Anhui, China) of Dendrobium officinale’s dried stem called Tiepi fengdou by mean of the handheld near-infrared spectrometer. Raw data were preprocessed to reduce unwanted spectral variations by the first-order derivative followed by standard normal variate transformation, and partial least squares discriminant analysis model was developed for calibration. The results showed that more than 90% of the origins were identified. Therefore, it is possible to classify the geographical origin of Tiepi fengdou by the use of the handheld near-infrared spectrometer for effective quality control.

A novel method for geographical origin identification ofTetrastigma hemsleyanum(Sanyeqing) by near-infrared spectroscopy

2018 ◽  
Vol 10 (25) ◽  
pp. 2980-2988 ◽  
Author(s):  
Weiqun Lin ◽  
Qinqin Chai ◽  
Wu Wang ◽  
Yurong Li ◽  
Bin Qiu ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Geographical Origin ◽  
Medicinal Herb ◽  
Tetrastigma Hemsleyanum ◽  
Novel Method ◽  
Origin Identification

Tetrastigma hemsleyanumDiels et Gilg (T. hemsleyanum), also known as Sanyeqing in Chinese, is a rare medicinal herb.

scholarly journals Determination of the Geographical Origin of Hazelnuts (Corylus avellana L.) by Near-Infrared Spectroscopy (NIR) and a Low-Level Fusion with Nuclear Magnetic Resonance (NMR)

2021 ◽  
Author(s):  
Navid Shakiba ◽  
Annika Gerdes ◽  
Nathalie Holz ◽  
Sören Wenck ◽  
René Bachmann ◽  
...  
Keyword(s):  
Data Fusion ◽  
Near Infrared ◽  
Geographical Origin ◽  
Nir Spectroscopy ◽  
Test Accuracy ◽  
Classification Models ◽  
Food Fraud ◽  
Low Level ◽  
The Individual

Fourier-transform near-infrared (FT-NIR) spectroscopy was used to determine the geographical origin of 233 hazelnut samples of various varieties from five different countries (Germany, France, Georgia, Italy, Turkey). The experimental determination of the geographical origin of hazelnuts is important, because there are usually large price differences between the producer countries and thus a risk of food fraud that should not be underestimated. The present work is a feasibility study using a low-cost method, as high-field NMR and UPLC-QTOF-MS have already been used for this question. Sample sets were split with repeated nested cross validation and an ensemble of discriminant classifiers with random subspaces was used to build the classification models. By using a preprocessing strategy consisting of multiplicative scatter correction, bucketing and the mean averaging of five measured spectra per sample, a test accuracy of 90.6 ± 3.9% was achieved, which rivals results obtained with much more expensive infrastructure. The application of the feature selection approach surrogate minimal depth showed that the successful classification is mainly caused by protein signals. In addition, a low-level data fusion of the NIR and NMR data was performed to assess how well the two methods complement each other. The data fusion was compared to a complementary approach, where the classification results based on the individual NIR and NMR models were jointly examined. The data fusion performed better than the individual methods with a test accuracy of 96.6 ± 2.8%. A comparison of the outliers in all classification models shows conspicuities in always the same samples, indicating that robust classification models are obtained.

Development of a Sample Set for Soya Bean Calibration of near Infrared Reflectance Spectroscopy

1994 ◽  
Vol 2 (4) ◽  
pp. 223-227 ◽  
Author(s):  
T.L. Hong ◽  
S.-J. Tsai ◽  
S.C.S. Tsou
Keyword(s):  
Near Infrared ◽  
Analytical Data ◽  
Storage Period ◽  
Nir Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Soya Bean ◽  
Chemical Compositions ◽  
Near Infrared Reflectance ◽  
Sample Set ◽  
Calibration Equations

The potential application of near infrared (NIR) spectroscopy is limited since its calibration equations are not always transferable from one instrument to another. Hence, an attempt was made to develop a selected sample set of soya beans with analytical data, which could be distributed to collaborators to calibrate their instruments. Sixty soya bean samples, (1 kg each) were selected and packed (200 g each) in laminated film bags after thorough mixing. During their storage at 4°C, the soya bean samples were periodically evaluated by chemical analysis as well as by NIR spectroscopy. Chemical compositions (i.e. moisture, protein and fat) were determined using conventional methods. Experimental results showed that no significant differences were found in the compositions of interest as well as in the reflectance spectra over a storage period of up to three years, and that the NIR spectroscopy method is independent of the location and model of the instruments. The experiment demonstrated that it is possible practically to use a pre-packed sample set with chemical data for calibration purposes.

scholarly journals Fingerprint Approaches Coupled with Chemometrics to Discriminate Geographic Origin of Imported Salmon in China’s Consumer Market

Foods ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 2986
Author(s):  
Xianshu Fu ◽  
Xuezhen Hong ◽  
Jinyan Liao ◽  
Qingge Ji ◽  
Chaofeng Li ◽  
...  
Keyword(s):  
Near Infrared ◽  
Geographical Origin ◽  
Geographic Origin ◽  
Nir Spectroscopy ◽  
Mineral Elements ◽  
Mineral Element ◽  
Consumer Market ◽  
Linear Discriminant ◽  
Discrimination Rate ◽  
Rapid Discrimination

Of the salmon sold in China’s consumer market, 92% was labelled as Norwegian salmon, but was in fact was mainly imported from Chile. The aim of this study was to establish an effective method for discriminating the geographic origin of imported salmon using two fingerprint approaches, Near-infrared (NIR) spectroscopy and mineral element fingerprint (MEF). In total, 80 salmon (40 from Norway and 40 from Chile) were tested, and data generated by NIR and MEF were analysed via various chemometrics. Four spectral preprocessing methods, including vector normalization (VN), Savitzky Golay (SG) smoothing, first derivative (FD) and second derivative (SD), were employed on the raw NIR data, and a partial least squares (PLS) model based on the FD + SG9 pretreatment could successfully differentiate Norwegian salmons from Chilean salmons, with a R2 value of 98.5%. Analysis of variance (ANOVA) and multiple comparative analysis were employed on the contents of 16 mineral elements including Pb, Fe, Cu, Zn, Al, Sr, Ni, As, Cr, V, Se, Mn, K, Ca, Na and Mg. The results showed that Fe, Zn, Al, Ni, As, Cr, V, Se, Ca and Na could be used as characteristic elements to discriminate the geographical origin of the imported salmon, and the discrimination rate of the linear discriminant analysis (LDA) model, trained on the above 10 elements, could reach up to 98.8%. The results demonstrate that both NIR and MEF could be effective tools for the rapid discrimination of geographic origin of imported salmon in China’s consumer market.

scholarly journals Rapid Geographical Origin Identification and Quality Assessment of Angelicae Sinensis Radix by FT-NIR Spectroscopy

2021 ◽  
Vol 2021 ◽  
pp. 1-12 ◽  
Author(s):  
Zhen-yu Zhang ◽  
Ying-jun Wang ◽  
Hui Yan ◽  
Xiang-wei Chang ◽  
Gui-sheng Zhou ◽  
...  
Keyword(s):  
Ferulic Acid ◽  
Near Infrared ◽  
Geographical Origin ◽  
Nir Spectroscopy ◽  
Quantitative Model ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Spectral Interval ◽  
Qualitative Model ◽  
Angelicae Sinensis

Angelicae Sinensis Radix is a widely used traditional Chinese medicine and spice in China. The purpose of this study was to develop a methodology for geographical classification of Angelicae Sinensis Radix and determine the contents of ferulic acid and Z-ligustilide in the samples using near-infrared spectroscopy. A qualitative model was established to identify the geographical origin of Angelicae Sinensis Radix using Fourier transform near-infrared (FT-NIR) spectroscopy. Support vector machine (SVM) algorithms were used for the establishment of a qualitative model. The optimum SVM model had a recognition rate of 100% for the calibration set and 83.72% for the prediction set. In addition, a quantitative model was established to predict the content of ferulic acid and Z-ligustilide using FT-NIR. Partial least squares regression (PLSR) algorithms were used for the establishment of a quantitative model. Synergy interval-PLS (Si-PLS) was used to screen the characteristic spectral interval to obtain the best PLSR model. The coefficient of determination for calibration (R2C) for the best PLSR models established with the optimal spectral preprocessing method and selected important spectral regions for the quantitative determination of ferulic acid and Z-ligustilide was 0.9659 and 0.9611, respectively, while the coefficient of determination for prediction (R2P) was 0.9118 and 0.9206, respectively. The values of the ratio of prediction to deviation (RPD) of the two final optimized PLSR models were greater than 2. The results suggested that NIR spectroscopy combined with SVM and PLSR algorithms could be exploited in the discrimination of Angelicae Sinensis Radix from different geographical locations for quality assurance and monitoring. This study might serve as a reference for quality evaluation of agricultural, pharmaceutical, and food products.

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